CN104301389A - Energy efficiency monitoring and managing method and system of cloud computing system - Google Patents
Energy efficiency monitoring and managing method and system of cloud computing system Download PDFInfo
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- CN104301389A CN104301389A CN201410481851.2A CN201410481851A CN104301389A CN 104301389 A CN104301389 A CN 104301389A CN 201410481851 A CN201410481851 A CN 201410481851A CN 104301389 A CN104301389 A CN 104301389A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
- H04L41/0833—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for reduction of network energy consumption
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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Abstract
The invention relates to an energy efficiency monitoring and managing method of a cloud computing system and an energy efficiency monitoring and managing system of the cloud computing system. According to the energy efficiency monitoring and managing method of the cloud computing system, a physical server and a monitoring agency service operating on a virtual machine initiatively send monitoring data to a domain monitoring data aggregation node in a timing mode, the complexity of the cloud computing energy efficiency monitoring system is lowered by introducing the domain monitoring data aggregation node, the load of a control node server is lowered, and the network congestion caused by burst network communication is avoided. By means of the energy efficiency monitoring and managing method of the cloud computing system, the support is provided for formulating a virtual machine dynamic migration plan according to the energy consumption condition which is estimated by inputting the monitoring data of the physical server and the virtual machine into an estimation model for the energy consumption of the server, and therefore energy consumption is reduced.
Description
Technical field
The present invention relates to the energy consumption monitoring in cloud computing and management domain, more particularly, relate to a kind of efficiency monitor and managment method of cloud computing system, and a kind of efficiency controlling and management of cloud computing system.
Background technology
Cloud computing is a kind of pattern of paying by use amount, this pattern provides access to netwoks available, easily, as required, (resource comprises network to enter configurable computing resource sharing pond, server, stores, application software, service), these resources can by Quick confession, only need drop into little management work, or carries out little mutual with service supplier.
Cloud computing deployment model is divided into 4 classes, comprising: publicly-owned cloud, privately owned cloud, community's cloud and mixed cloud.The cloud infrastructure of publicly-owned cloud is open to whole the Internet.It is runed by a cloud computing service provider (commercial undertaking, educational institution or government organs), provides cloud service by the Internet to user, and anyone has authority that is paid or use without compensation.Relatively publicly-owned cloud, privately owned cloud is not opened to the outside world or limited opening, completes construction, management and by certain tissue or enterprise.It uses object to be mainly positioned at organization internal.The high in the clouds infrastructure that community's cloud is shared by several tissue is formed.It supports specific community, possesses common item deeply concerned, as: task object, demand for security etc.Manager can be tissue itself, also can be third party; Gerentocratic position may be positioned at organization internal, also may be positioned at tissue outside.Mixed cloud combines two kinds or two or more cloud computing modes, can make the mixing of privately owned cloud and publicly-owned cloud, also can be the mixing of privately owned cloud and community's cloud.As: privately owned cloud can obtain computational resource supplementing as himself infrastructure from publicly-owned cloud.Mixed cloud can provide a unified interface or management plane, and for end user, the service obtained from different cloud computing modes has consistency.
The high energy consumption of data center is distinct issues always.In the particularly current cloud computing epoch, more calculating, storage resources concentrate on high in the clouds, bring larger challenge to the efficient management of energy consumption.Research shows: the enterprise in the whole world will spend about 40,000,000,000 dollars every year for energy consumption.The high energy consumption problem of data center not only causes the waste of electric energy, also damages environment.Mechanism of the United States Federal has pointed out that high energy consumption problem will cause aspects such as air quality, national security, climate change, electric network reliabilities and has had a strong impact on.Therefore, the data center in cloud computing epoch energy-conservation very urgent, energy optimization management has become cloud epoch data center major issue urgently to be resolved hurrily.
One of most effective means improving efficiency of energy utilization is exactly use less server to perform calculating, and such as multiple application program is merged on individual server and runs simultaneously by use Intel Virtualization Technology.The potentiality of Server Consolidation are surprising.The total resources occupation rate running the traditional physical server of single enterprise's application approximately only has 5% to 15%, this means that identical server is had the ability carrying 10 virtual machines, even if average each virtual machine uses 8% of the total computing capability of server, total this server still can retain 20% of computing capability after replacement 10 physical servers.
Server Consolidation is also not equal to whole integration, and should decide how to integrate according to the feature of operating load, the technology used and support team.The enterprise just having contacted Intel Virtualization Technology from non-key application program, first should carry out limited virtualization integration, then improves integrative levels and virtual prior operating load gradually.Due to needs virtual machine is followed the tracks of, monitoring and controlling, the virtual importance improving system management facility and practice.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art, a kind of efficiency monitor and managment method of cloud computing system of energy efficient is provided, and a kind of efficiency controlling and management of cloud computing system.
Technical scheme of the present invention is as follows:
An efficiency monitor and managment method for cloud computing system, step is as follows:
1) by operating in the monitoring agent service on physical server and virtual machine, the monitor data of respective physical server and virtual machine Key Performance Indicator is obtained;
2) Controlling vertex is sent to by territory monitor data aggregation node by unified for monitor data;
3) by after the data receiver service acquisition operated on Controlling vertex, resource load monitor service and energy consumption monitoring service is sent to;
4) by energy consumption and resource load situation input resource allocation decisions device, generating virtual machines dynamic migration strategy, and move actuator execution virtual machine (vm) migration strategy by VM;
5) after having moved, the physical server of zero load is placed in resting state.
As preferably, before generating migration strategy, monitor data is inputted Estimation of energy consumption model, estimation energy consumption.
As preferably, the step of the energy consumption of estimation virtual machine and server is as follows:
A) set up energy consumption model, all types of resource utilizations and total system energy consumption are set up contact;
B) all types of resource utilization when each virtual machine runs measured by usage monitoring instrument;
C) energy consumption of virtual machine situation is assessed.
As preferably, the total energy consumption E of virtual machine and server
server=E
idle+ E
cpu+ E
mem+ E
diek+ E
net, wherein, E
cpufor CPU energy consumption, E
memfor internal memory energy consumption, E
diskfor disk energy consumption, E
idlefor server free energy consumption, E
netfor server network energy consumption.
As preferably, the energy consumption model of CPU energy consumption is E
cPU=α
cPUu
cPU+ γ
cPU, wherein, u
cPUrepresent processor utilization, determined by the movable number of times of processor and dormancy number of times, α
cPUand γ
cPUrepresent the fixed constant of CPU energy consumption model.
As preferably, by recording the disappearance number of times determination internal memory energy consumption of last one deck Cache, internal memory energy consumption model is: E
mem(T)=α
memn
lLC(T)+γ
mem, wherein, E
mem(T) total energy consumption of internal memory in T time is represented, N
lLC(T) represent that in T time, LLC lacks number of times, α
memand γ
memrepresent the fixed constant of internal memory energy consumption model.
As preferably, hard disk energy consumption model is: E
disk(T)=α
rbb
r+ α
wbb
w+ γ
disk, set up with joint number of writing by the byte number of reading obtaining hard disk, wherein, E
disk(T) energy consumption of hard disk in T time is represented, b
rrepresent the byte number read in T time, b
wrepresent the byte number write in T time. α
rb, α
wband γ
diskfor the fixed constant of hard disk energy consumption model.
As preferably, the energy consumption presetting disk read-write is identical, then hard disk energy consumption model is: E
disk(T)=α
10× b
10+ γ
disk, wherein, b
10represent the total bytes of read and write, α
10for the fixed constant of hard disk energy consumption model.
An efficiency controlling and management for cloud computing system, comprises cloud computing resource pool, Controlling vertex, territory monitor data aggregation node, VM moves actuator; Cloud computing resource pool comprises multiple server admin territory, and server admin territory comprises multiple physical server, and physical server comprises multiple virtual machine and monitoring agent service that it runs, and virtual machine comprises the monitoring agent service that it runs; Monitor data, on monitored physical server and virtual machine, for obtaining the running status of physical server and virtual machine, is sent to territory monitor data aggregation node by monitoring agent service operation.
As preferably, territory monitor data aggregation node comprises its monitor data and receives service and Data Integration service; VM moves actuator and comprises migration operation n-back test and transition state rotary function; Controlling vertex, comprises data receiver service, resource load monitor service, energy consumption monitoring service and resource allocation decisions device;
Wherein, data receiver service is used for the physical server of acceptance domain convergence node transmission and the monitor data of virtual machine, and the monitor data received is sent to resource load monitor service and energy consumption monitoring service;
Resource load monitor service is used for the loading condition generating physical server according to monitor data;
Energy consumption monitoring service is used for monitor data being inputted energy consumption model estimation energy consumption.
Resource allocation decisions device is used for according to loading condition and energy consumption generating virtual machines migration strategy.
Beneficial effect of the present invention is as follows:
According to the efficiency monitor and managment method of cloud computing system of the present invention, the monitoring agent service wherein physical server and virtual machine run initiatively and timing sends monitor data to territory monitor data aggregation node, by introducing territory monitor data aggregation node, reduce the complexity of cloud computing energy efficiency monitoring system, the network congestion that the load reducing Controlling vertex server and the network service avoiding burst cause.
Adopt the efficiency monitor and managment method of cloud computing system of the present invention, according to the energy consumption that the assessment models of the monitor data of physical server and virtual machine input server energy consumption is estimated, for the formulation of dynamic migration of virtual machine plan provides support, thus energy efficient.
Enforcement of the present invention, enterprise can virtual existing server zone easily, and the replacement period then immediately following subsequent technology carries out orderly upgrading to server system.Server updating has an opportunity to optimize and combine work by IT team, makes virtual machine distribution between servers more balanced.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of method of the present invention;
Fig. 2 is that schematic diagram is implemented in the scheduling of system of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.
The invention provides a kind of efficiency monitor and managment method of cloud computing system, as shown in Figure 1, concrete steps are as follows:
Step 1, serves the monitor data of periodic collection physical server and virtual machine by the monitoring agent operated on physical server and virtual machine;
Step 2, inputs Estimation of energy consumption model by monitor data, estimation energy consumption;
Step 3, according to load monitoring data and the Estimation of energy consumption result formulation virtual machine (vm) migration strategy of server;
Step 4, judges whether virtual machine place physical server current task is finished, if be finished, then enters step 5, otherwise waits for that physical server is finished, and enters step 4;
Step 5, selects the target physical server of virtual machine (vm) migration in server admin territory;
Step 6, performs virtual machine (vm) migration plan;
Step 7, judges that whether virtual machine (vm) migration is successful, if success, then enters step 8, otherwise, enter step 5;
Step 8, physical server enters park mode, returns step 1.
Before generating migration strategy, monitor data is inputted Estimation of energy consumption model, estimation energy consumption.The basic ideas of the energy consumption measurement of virtual machine and server are as follows: first, set up energy consumption model, all types of resource utilizations (as: cpu busy percentage) and total system energy consumption are set up contact, does not consider the energy consumption of other resource type being in relatively low utilance level for the sake of simplicity; Then, use lightweight monitoring tools to measure the utilance of different resource when each virtual machine runs, the hardware performance counter that such as can be provided by typical virtual platform (as KVM) carries out online edge analysis; Finally, energy consumption of virtual machine situation is assessed.Input resource utilization, by the calculating of resource energy consumption model, infers the energy consumption of virtual machine indirectly.Based on above thinking, the total energy consumption of system can be expressed as:
E
server＝E
idle+E
cpu+E
mem+E
disk+E
net；
Wherein, E
cpufor CPU energy consumption, E
memfor internal memory energy consumption, E
diskfor disk energy consumption, E
idlefor server free energy consumption, E
netfor server network energy consumption.
CPU energy consumption model, depends in multiple factor, as the active situation of CPU subelement, and the situation that instruction performs, the service condition of buffer memory in processor, and the height of processor present operating frequency.Accurate CPU energy consumption model, needs to consider all factors, and the expense of monitoring can be made excessive, so be not suitable for the real time energy consumption assessment of CPU.And the activity of tracking processor and dormancy number of times, can obtain from operating system.Energy consumption model for the CPU of well known processor utilance is as follows:
E
CPU＝α
CPUu
CPU+γ
CPU；
Wherein, u
cPUrepresent processor utilization, α
cPUand γ
cPUrepresent the fixed constant of CPU energy consumption model, according to the CPU of different model, can be obtained by the experimental technique of prior art.
If the processor utilization of a virtual machine A is expressed as u
cPU, A, so energy consumption E of this virtual machine
cPU, Afor:
E
CPU，A＝α
CPUu
CPU，A。
Internal memory energy consumption model, determines that the principal element of internal memory energy consumption is memory read-write throughput.Have a kind of appraisal procedure of internal memory throughput of lightweight, namely record the disappearance number of times of last one deck Cache (Last Level Cache), this is easy to obtain on most of processor.Use these indexs, the energy consumption model of internal memory can be written as following form:
E
mem(T)＝α
memN
LLC(T)+γ
mem；
Wherein, E
mem(T) total energy consumption of internal memory in T time is represented, N
lLC(T) represent that in T time, LLC lacks number of times, α
memand γ
memrepresent the preset parameter of internal memory energy consumption model, according to the internal memory of different model and capacity, obtained by the experimental technique of prior art.
Because internal storage access is directly managed by hardware logic unit, operating system and Hypervisor can not directly access, so the LLC disappearance of directly following the tracks of virtual machine is not easy to realize.Most processor lacks a performance counter as hardware, as Intel Nehalem processor provides this function on each core using LLC.The LLC caused because of contextual processing on each core by following the tracks of each virtual machine lacks number of times, and we just can obtain the disappearance number of times of the LLC of respective virtual machine.Therefore the internal memory energy consumption model of virtual machine A is as follows:
E
mem，A(T)＝α
memN
LLC，A(T)；
Wherein, E
mem, A(T) represent that the LLC of a virtual machine A in T time on all core lacks number of times, α
memrepresent model parameter.
Disk energy consumption model, can use by Hypervisor get hard disk read byte number and joint number of writing sets up energy consumption model:
E
disk(T)＝α
rbb
r+α
wbb
w+γ
disk；
Wherein, E
disk(T) energy consumption of hard disk in T time is represented, b
rrepresent the byte number read in T time, b
wrepresent the byte number write in T time. α
rb, α
wband γ
diskrepresent the fixed constant of disk energy consumption model, according to the disk of different model and capacity, obtained by the experimental technique of prior art.
When hard disk is in active state, virtual machine is not necessarily in active state, because Hypervisor may carry out batch processing I/O interruption at this moment, or buffer memory I/O operates.Therefore, tracking I/O explicit in Hypervisor is needed to operate.Therefore, the disk energy consumption model of following virtual machine A can be obtained:
E
disk，A＝α
rbb
r，A+α
wbb
w，A；
Wherein, b
r, Aand b
w, Arepresent the byte number of virtual machine A read and write.Experimentally conclusion, can ignore the energy consumption difference of hard disk read and write.So the parameter that employing one is common, b
10represent the total bytes of read and write, α
10for model parameter, therefore can be model simplification:
E
disk(T)＝α
10×b
10+γ
disk；
The hard disk energy consumption of corresponding virtual machine A is:
E
disk，A＝α
10×b
10，A；
Wherein, b
10, Arepresent the total bytes of the read and write of virtual machine A, α
10for the preset parameter of disk energy consumption model, according to different model and capacity disc, obtained by the experimental technique of prior art.
Present invention also offers a kind of cloud computing energy consumption monitoring and management system, as shown in Figure 2, comprising: comprise cloud computing resource pool, Controlling vertex, territory monitor data aggregation node, VM move actuator, jointly for the treatment of monitor data and migration virtual machine.
Cloud computing resource pool comprises multiple server admin territory (server admin territory D
1server admin territory D
n);
Server admin territory comprises multiple physical server (physical server D
1s
1physical server D
1s
q, physical server D
ns
1physical server D
ns
q);
Physical server comprises multiple virtual machine and monitoring agent service that it runs;
Virtual machine comprises the monitoring agent service that it runs.
Wherein, described monitoring agent service, runs on monitored service server and virtual machine, for obtaining the running status of physical server and virtual machine, monitor data is sent to territory monitor data aggregation node.
Controlling vertex comprises data receiver service, resource load monitor service, energy consumption monitoring service and resource allocation decisions device.
Wherein, described data receiver service, the physical server sent for acceptance domain convergence node and the monitor data of virtual machine, and the monitor data received is sent to resource load monitor service and energy consumption monitoring service;
Described resource load monitor service, for generating the loading condition of physical server according to monitor data;
Described energy consumption monitoring service, for inputting energy consumption model estimation energy consumption by monitor data;
Described resource allocation decisions device, for according to loading condition and energy consumption generating virtual machines migration strategy.
Territory monitor data aggregation node, comprises its monitor data and receives service and Data Integration service.
VM moves actuator, comprises migration operation n-back test and transition state rotary function.
In cloud computing resource pool, the physical server of some (n platform) is divided into a server admin territory, is convenient to managing physical server and collection monitoring data.
Physical server in server admin territory and the virtual machine operation monitoring agency service wherein run, be sent to territory monitor data aggregation node by the monitor data of physical server and virtual machine.Territory monitor data aggregation node is sent to the data receiver service of Controlling vertex after being integrated by the monitor data received.
The data receiver service of Controlling vertex is for receiving the monitor data of each territory monitor data aggregation node transmission, and the monitor data after integrating is sent to resource load monitor service and energy consumption monitoring service respectively, wherein resource load monitor service exports the loading condition of physical server, and wherein energy consumption monitoring service exports the energy consumption of physical server and virtual machine according to Estimation of energy consumption model.Loading condition and energy consumption input resource allocation decisions device, formulate dynamic migration of virtual machine plan, VM migration operation order being outputted to corresponding server management domain moves actuator.
After VM migration actuator receives migration operation order, perform virtual machine (vm) migration operation.After virtual machine (vm) migration completes, corresponding physical server is placed in resting state, energy efficient.
Above-described embodiment is only used to the present invention is described, and is not used as limitation of the invention.As long as according to technical spirit of the present invention, change above-described embodiment, modification etc. all will be dropped in the scope of claim of the present invention.
Claims (10)
1. an efficiency monitor and managment method for cloud computing system, it is characterized in that, step is as follows:
1) by operating in the monitoring agent service on physical server and virtual machine, the monitor data of respective physical server and virtual machine Key Performance Indicator is obtained;
2) Controlling vertex is sent to by territory monitor data aggregation node by unified for monitor data;
3) by after the data receiver service acquisition operated on Controlling vertex, resource load monitor service and energy consumption monitoring service is sent to;
4) by energy consumption and resource load situation input resource allocation decisions device, generating virtual machines dynamic migration strategy, and move actuator execution virtual machine (vm) migration strategy by VM;
5) after having moved, the physical server of zero load is placed in resting state.
2. the efficiency monitor and managment method of cloud computing system according to claim 1, is characterized in that, before generating migration strategy, monitor data is inputted Estimation of energy consumption model, estimation energy consumption.
3. the efficiency monitor and managment method of cloud computing system according to claim 2, is characterized in that, the step of the energy consumption of estimation virtual machine and server is as follows:
A) set up energy consumption model, all types of resource utilizations and total system energy consumption are set up contact;
B) all types of resource utilization when each virtual machine runs measured by usage monitoring instrument;
C) energy consumption of virtual machine situation is assessed.
4. the efficiency monitor and managment method of cloud computing system according to claim 3, is characterized in that, the total energy consumption E of virtual machine and server
servere
idle+ E
cpu+ E
mem+ E
disk+ E
net, wherein, E
cpufor CPU energy consumption, E
memfor internal memory energy consumption, E
diekfor disk energy consumption, E
idlefor server free energy consumption, E
netfor server network energy consumption.
5. the efficiency monitor and managment method of cloud computing system according to claim 4, is characterized in that, the energy consumption model of CPU energy consumption is E
cPU=α
cPUu
cPU+ γ
cPU, wherein, u
cPUrepresent processor utilization, determined by the movable number of times of processor and dormancy number of times, α
cPUand γ
cPUrepresent the fixed constant of CPU energy consumption model.
6. the efficiency monitor and managment method of cloud computing system according to claim 4, is characterized in that, by recording the disappearance number of times determination internal memory energy consumption of last one deck Cache, internal memory energy consumption model is: E
mem(T)=α
memn
lLC(T)+γ
mem, wherein, E
mem(T) total energy consumption of internal memory in T time is represented, N
lLC(T) represent that in T time, LLC lacks number of times, α
memand γ
memrepresent the preset parameter of internal memory energy consumption model.
7. the efficiency monitor and managment method of cloud computing system according to claim 4, it is characterized in that, hard disk energy consumption model is: E
disk(T)=α
rbb
r+ α
wbb
w+ γ
disk, set up with joint number of writing by the byte number of reading obtaining hard disk, wherein, E
disk(T) energy consumption of hard disk in T time is represented, b
rrepresent the byte number read in T time, b
wrepresent the byte number write in T time.α
rb, α
wband γ
diskfor the fixed constant of hard disk energy consumption model.
8. the efficiency monitor and managment method of cloud computing system according to claim 7, is characterized in that, the energy consumption presetting disk read-write is identical, then hard disk energy consumption model is: E
disk(T)=α
10× b
10+ γ
disk, wherein, b
10represent the total bytes of read and write, α
10for the fixed constant of hard disk energy consumption model.
9. an efficiency controlling and management for cloud computing system, is characterized in that, comprises cloud computing resource pool, Controlling vertex, territory monitor data aggregation node, VM move actuator; Cloud computing resource pool comprises multiple server admin territory, and server admin territory comprises multiple physical server, and physical server comprises multiple virtual machine and monitoring agent service that it runs, and virtual machine comprises the monitoring agent service that it runs; Monitor data, on monitored physical server and virtual machine, for obtaining the running status of physical server and virtual machine, is sent to territory monitor data aggregation node by monitoring agent service operation.
10. the efficiency controlling and management of cloud computing system as claimed in claim 9, is characterized in that, territory monitor data aggregation node comprises its monitor data and receives service and Data Integration service; VM moves actuator and comprises migration operation n-back test and transition state rotary function; Controlling vertex, comprises data receiver service, resource load monitor service, energy consumption monitoring service and resource allocation decisions device;
Wherein, data receiver service is used for the physical server of acceptance domain convergence node transmission and the monitor data of virtual machine, and the monitor data received is sent to resource load monitor service and energy consumption monitoring service;
Resource load monitor service is used for the loading condition generating physical server according to monitor data;
Energy consumption monitoring service is used for monitor data being inputted energy consumption model estimation energy consumption.
Resource allocation decisions device is used for according to loading condition and energy consumption generating virtual machines migration strategy.
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